This invention relates generally to evaluating a machine parameter trend as it approaches an impending failure limit or point of unacceptable performance and reacting thereto, and more particularly, to a method and apparatus for determining a severity value for the trend, identifying the causal event or events thereof, and responding to the same.
For service and diagnostic purposes, machines are sometimes equipped with sensors for measuring operating conditions such as engine RPM, oil pressure, water temperature, boost pressure, oil contamination, electric motor current, hydraulic pressure, system voltage, and other like parameters. In some cases, storage devices are provided to compile a data base for later evaluation of machine performance and to aid in diagnosis. Service personnel examine the accrued data to get a better picture of the causes of any machine performance degradation, wear or failure. Similarly, service personnel evaluate the stored data to predict future failures and associated collateral damages, and to correct any problems before total component failure.
In addition, these stored parameters may be examined by service or supervisory personnel to evaluate machine and/or operator performance to ensure maximum productivity of the machine. These issues are particularly pertinent to over-the-highway trucks and large work machines such as off-highway mining trucks, hydraulic excavators, track-type tractors, wheel loaders, and the like. These machines represent large capital investments and are capable of substantial productivity when operating. It is therefore important to predict significant performance loss, wear and catastrophic failures so servicing can be scheduled during periods in which productivity will be less affected, so that the machine can be moved to a lighter duty, or so minor problems can be repaired before they lead to catastrophic failures.
Similarly, it is sometimes advantageous to accumulate parameters only when the machine is in a particular operating condition. This type of information is predominantly used during performance evaluation but may also be used in failure diagnosis and prognosis. For example, the length of time spent in a particular gear while the machine is loaded may be needed to evaluate machine performance.
Currently, numerous methods and apparatus are known for determining machine operating parameter trends for predicting and diagnosing machine fault conditions.
Reference, for instance, Schricker et al. U.S. Pat. No. 5,561,610, issued Oct. 1, 1996 to Caterpillar, Inc. which discloses linear curve fitting techniques for identifying trends for predicting fault conditions. Also reference Kondo et al. European Patent Application No. 81303100.2 published Jan. 13, 1982, assigned to Nippondenso Co., Ltd., which discloses methods and an apparatus for forecasting and giving warning of automotive abnormalities characterized by calculating a rate of change of a detection signal, forecasting the occurrence of an abnormality based on the rate of change, and responsively issuing an alarm.
However, the referenced known methods and apparatus fail to identify the root cause or causes for increases in the severity of failure trends or abnormalities, to enable responding to the same.
For instance, with information relating to the cause of an increase in severity of a trend toward an impending machine failure, service personnel would be able to take steps to eliminate or reduce the cause so as to decrease the severity and thus prolong the remaining service life of the machine. Also, using information regarding causation of a severity increase, machine manufacturers and dealers would be able to more accurately determine warranty costs and limitations for service contracts and the like. Such information would also enable better planning of maintenance schedules and machine downtime for service and repair.
Accordingly, the present invention is directed to overcoming one or more of the problems as set forth above.
In one aspect of the present invention a method for determining severity of a trend toward an impending machine failure under actual or proposed operating conditions is disclosed, including the steps of:
(a) providing a typical failure trend based on a set of normal operating conditions for the machine;
(b) determining a slope value for the typical failure trend;
(c) determining a slope value for the trend toward the impending machine failure under the actual or proposed operating conditions;
(d) determining a weight value for at least the actual or proposed operating conditions; and
(e) determining the severity as a function of the slope values and the weight value. According to an additional aspect of the invention apparatus for performing the above method is also disclosed.
According to the invention, the weight value for the actual or proposed operating conditions can be determined from load data, temperature data, pressure variation data, environmental data, application data, as well as other pertinent information. Additionally, a weight value can be determined for the typical failure trend and the severity determined as a function of the slopes and the weight values for both the normal operating conditions and the actual or proposed operating conditions.
The severity can then be compared to a predetermined typical severity profile value, and, if greater than the typical profile, a problem is identified and the severity can be associated with the current events or conditions to determine a root cause for the severity. For instance, if the severity determination relates to oil temperature, sensor data would be reviewed or a physical check of the machine could be made to determine if related events or conditions are present. For instance, a determination could be made whether the cooling fan was operational or a coolant line or a radiator was clogged. Appropriate personnel could then be alerted or other action taken, such as corrective action or some step to alleviate the condition such as moving the machine to an easier job or removing an abusive operator.
The present invention also has utility for failure diagnosis and prognosis, and can be used for more accurately determining warranty cost and limitations for applications involving unusual operating conditions.